Capture what compute leaves behind.
The Charge Controller accumulates inference potential from completed workloads, adapting each Charge Cycle to your capacity envelope.
INTRODUCING THE CAPACITANCE RUNTIME ↗Build inference
Build inference
capacity before
you need it.
A software-defined capacity layer for AI inference.
Accumulate potential on your existing compute.
Make it available to whatever comes next.
YOUR INFRASTRUCTURE. YOUR MODELS. RETAINED POTENTIAL.
IC / RUNTIME 01● ADAPTIVE
STORED INFERENCE POTENTIAL14.7 TiR82% CAPACITANCE UTILIZED
CHARGE
+0.24 TiR/s
+0.24 TiR/s
DISCHARGE
READY ↗
READY ↗
SOFTWARE-DEFINED RESERVOIRRETENTION 72H
A NEW LAYER.
YOUR EXISTING STACK.
YOUR EXISTING STACK.
vLLM
Amazon Bedrock
Kubernetes
GPU clusters
Model APIs ↗
01 / THE CAPACITY LAYERDECOUPLE GENERATION FROM CONSUMPTION
Compute is temporary.
Inference capacity
shouldn’t be.
Every inference workload generates computational potential that traditional runtimes simply discard. Inference Capacitance captures that potential at the middleware layer and makes it reusable across future workloads.
YOUR INFRASTRUCTURE PRIVATE BY DEPLOYMENT
Applications / AgentsYour prompts. Your orchestration.
INFERENCE CAPACITANCE™Runtime
ChargeRetainRoute
Your ComputevLLM · Bedrock · GPUs · APIs
DEPLOYED IN YOUR CLUSTERNO HOSTED INFERENCE REQUIREDMODEL-AGNOSTIC BY DESIGN
Keep capacity beyond the request.
An Inference Capacitor™ preserves generalized reasoning potential inside a software-defined Dielectric Layer™, isolated by workload.
Meet demand before compute does.
Discharge retained potential into future requests. Burst Discharge absorbs demand spikes before live inference needs to scale.
Not a cache.
Not even a little.
Caches reuse outputs when inputs repeat. Inference Capacitance preserves computational potential independent of specific inputs, allowing future workloads to benefit even when prompts, context, and outputs differ.
CAPABILITYCacheCapacitance
Requires matching inputsYesNo
Retains completed responsesYesNo
Works across novel prompts—By design
Preserves inference potential—Continuously
DIFFERENT PROMPT→RETAINED POTENTIAL→ENTIRELY NEW RESPONSE
03 / CAPACITY, MADE OBSERVABLETHE RUNTIME CONSOLE
You can’t manage
what you don’t retain.
Reasoning capacity becomes an operational resource. Inspect potential, tune retention, and understand exactly how ready your infrastructure is for its next workload.
Stored Potential14.7 TiR+2.3 TiR this cycle
Charge82%
Leakage1.8%/hrWithin tolerance
Discharge ReadinessREADYVoltage nominal
Inference Reuse61.4%Across novel workloads
Retained potential TiRAvailable capacity LAST 24 HOURS
00:0006:0012:0018:00NOW
TiR = tera-inferences of retained reasoning potential. Normalized at nominal inference voltage; independent of response token count.
Demand is bursty.
Your capacity isn’t.
Traditional infrastructure purchases capacity at the moment of demand. A retained capacity layer smooths the compute envelope—without asking workloads to wait.
Live compute requiredNORMALIZED / 60 MIN
0 MIN15 MIN30 MIN45 MIN60 MIN
Traditional InferenceCapacitive Inference
−68%
Peak live-inference cost1
−43%
p95 response latency1
SAME MODELS. SAME WORKLOAD.
MORE CAPACITY BETWEEN REQUESTS.
MORE CAPACITY BETWEEN REQUESTS.
1. IC-Labs Capacity Envelope Study 26.09. Reference workload: 120k heterogeneous requests, 8× burst factor, 72h retention, adaptive mode. Compared with an unbuffered runtime at equivalent model quality. Figures represent modeled behavior in a synthetic reference environment; results depend on initial charge and discharge policy.
05 / INSTALLED, NOT RENTEDYour cluster.
Your cluster.
Your compute.
New potential.
Inference Capacitance is middleware you install between your applications and your inference backends. Start with a runtime in your Kubernetes cluster. Keep execution, policies, and retained potential inside your infrastructure.
HELM-NATIVEPROVIDER-AGNOSTICYOUR DATA PLANE
QUICKSTART / KUBERNETES
$ helm install capacitance \
oci://registry.inferencecap.io/runtimevalues.yaml
# Retain today. Reason tomorrow.
capacitance:
mode: adaptive
retention: 72h
leakageTolerance: 0.04
dischargePolicy: latency-firstA new operational vocabulary.
Inference Potential (IP)
Generalized reasoning capacity retained after completed computation, available to workloads that have yet to arrive.
Capacitance
The maximum inference potential a deployment can retain, expressed in TiR and governed by its software capacity envelope.
Inference Voltage™
The readiness gradient of retained potential. Higher voltage enables faster discharge into latency-sensitive requests.
Leakage
The gradual loss of stored inference relevance over time. Managed continuously against your retention policy.
Dielectric Layer™
A logical isolation boundary that prevents potential interference between retained workloads and tenant capacity pools.
Charge Controller
The adaptive scheduler that balances accumulation, retention, and discharge as workload conditions evolve.
07 / FREQUENTLY CONSIDEREDReasonable
Reasonable
questions.
What exactly is stored in an Inference Capacitor?+
Unlike conventional caches, Inference Capacitors do not store responses. They preserve generalized computational potential derived from completed inference work, allowing future workloads to consume previously accumulated reasoning capacity without requiring semantic equivalence.
Is this software, a compute service, or hardware?+
Software. The runtime is installed in your infrastructure and connects to your existing inference providers. It does not supply GPUs, store electrical energy, or require specialized hardware. Charge and discharge describe the lifecycle of inference potential.
What happens when retained potential runs out?+
The runtime routes requests to your configured compute backends. Completed inference replenishes the capacity pool through the next Charge Cycle. Applications continue using the same inference interface.
Does this require repeating prompts or similar context?+
No. Potential is retained independently of the input that generated it. Semantic similarity, prompt hashes, and response matching are not prerequisites for discharge.
How do I size an Inference Capacitor?+
Start with the potential consumed during your typical burst window. The Charge Controller calibrates nominal capacitance against workload entropy, retention horizon, and acceptable leakage. Adaptive mode continuously adjusts the usable envelope.
Can I use this alongside a response cache?+
Yes. A response cache resolves known requests. The capacitance layer makes accumulated potential available to new requests. Deploy both to retain outputs and the computational capacity beyond them.